Project Summary What produces an individual's unique set of traits, such as hair color, heart rate, or even probability of developing a disease? Most traits of healthy organisms are a combination of inherited genetic factors interacting with environmental conditions such as diet or stress levels. When these interactions go awry, the outcome is often a disease state in certain members of the population. Often, the source of the disease is a segregating variant of a gene that is harmful and which can disrupt the normal regulation or transcription of a gene or set of genes. Single letter changes, so-called Single Nucleotide Polymorphisms (SNPs), have long been recognized as an important category of genetic variation. Recently, with the advent of more high throughput genomic techniques, the prevalence of structural variation - such as inversions, deletions, or translocations of large blocks of DNA - has become increasingly appreciated. Importantly, initial studies have indicated that these structural variants can be associated with human diseases such as hemophilia and Hunter syndrome among many others. Structural variation can affect genomes and phenotypes by creating variation in gene copy number, and by exposing recessive alleles through gene deletions. However, the way that this genomic level variation can translate to differences in organismal traits is still unclear. A hypothesis is that structural variants may alter the regulation of genes, changing when, where and how much of a particular gene is made in each cell. Studying the mechanistic relationship between structural and phenotypic variation is difficult in humans, but can be accomplished in model vertebrate organisms such as mice or in our case natural populations of small fish. The primary goal of this research project is to document the genomic distribution of structural variants in natural populations and to test the hypothesis that genome level structural variation contributes to phenotypic variation through changes in gene expression. The research will focus primarily on threespine stickleback, an emerging model for dissecting the genetic basis of phenotypic variation in quantitative traits, and is amenable to both screens in natural populations and manipulative studies in the laboratory. We will use a novel synthesis of several whole-genome analysis and next generation sequencing techniques to accomplish the following aims. First, we will identify the distribution of structural variants within and among populations of ancestral and derived threespine stickleback fish, and second, we will genetically map the effects of structural variation on gene expression to infer cis-regulatory changes through an eQTL analysis. To make our analysis techniques widely available for use in other model organisms, we will create a set of public, web-based computational resources for identifying associating structural variation with gene expression.